@article{curran_sadeghi_mcbride_mattsson_pike_harrison_abdulmojeed_theiss_tomhon_chekmenev_et al._2025, title={Machine Learning-Guided Optimization of SABRE Hyperpolarization for α-Ketoglutarate in Acetone–Water}, DOI={10.1021/acs.analchem.5c05859}, abstractNote={Signal amplification by reversible exchange (SABRE) is a hyperpolarization method that polarizes target nuclei of metabolites quickly and efficiently. Recent SABRE advances, including Ace-SABRE, yield biocompatible, aqueous solutions of hyperpolarized markers for metabolic monitoring. Building on recent advancements, expanding the substrate scope of Ace-SABRE is desirable. However, SABRE polarization is sensitive to many different parameters; therefore, traditional optimization approaches are experimentally time-consuming. In this proof-of-concept application of machine learning (ML), Bayesian optimization (BO) is used for four important input parameters to model the complex SABRE dynamics while saving experimental time. The presented ML model also provides chemical insights that enable predictions of sample compositions for increased polarization levels. In this work, we transition from an original average free polarization of <i>p</i> = ∼0.90% to a maximum observed free polarization of <i>p</i> = ∼6.6% for 1-<sup>13</sup>C alpha-ketoglutarate (AKG) with <sup>13</sup>C at natural abundance, utilizing both direct outputs as well as chemical insights revealed by the ML model.}, journal={Analytical Chemistry}, author={Curran, Erica and Sadeghi, Sina and McBride, Stephen J. and Mattsson, Karl and Pike, Megan and Harrison, Rielly J. and Abdulmojeed, Mustapha B. and Theiss, Franziska and TomHon, Patrick and Chekmenev, Eduard Y. and et al.}, year={2025}, month={Dec} }
 @article{dhali_abdurraheem_abdulmojeed_samoilenko_pike_harrison_theiss_goodson_chekmenev_theis_2025, title={SABRE-SHEATH hyperpolarized <sup>15</sup>N<sub>2</sub>-imidazole for Zn<sup>2+</sup> sensing}, volume={7}, url={https://doi.org/10.1039/D5CC02890F}, DOI={10.1039/d5cc02890f}, abstractNote={SABRE-SHEATH hyperpolarized 15 N 2 -imidazole is introduced as a novel 15 N NMR probe for Zn 2+ sensing. The method achieves 45 700 fold signal enhancement and a detection limit of 1.3 mM, enabling chemical sensing without penetration-depth limitations.}, journal={Chemical Communications}, author={Dhali, Brojo Kishor Shachib and Abdurraheem, Abubakar and Abdulmojeed, Mustapha and Samoilenko, Anna and Pike, Megan and Harrison, Rielly J. and Theiss, Franziska and Goodson, Boyd M. and Chekmenev, Eduard Y. and Theis, Thomas}, year={2025}, month={Jan} }
 @article{mcbride_pike_curran_zavriyev_adebesin_tucker_harzan_senanayake_abdulmojeed_theiss_et al._2025, title={Scalable Hyperpolarized MRI Enabled by Ace‐SABRE of [1‐ <sup>13</sup> C]Pyruvate}, volume={7}, url={https://doi.org/10.1002/anie.202501231}, DOI={10.1002/anie.202501231}, abstractNote={Abstract Hyperpolarized (HP) MRI using [1– 13 C]pyruvate is emerging as a promising molecular imaging approach. Among hyperpolarization methods, Signal Amplification By Reversible Exchange (SABRE) is attractive because SABRE polarizes the substrates directly in room‐temperature solutions avoiding complex hardware. Most SABRE experiments have historically been performed in methanol, a relatively toxic and difficult‐to‐remove solvent. Here we demonstrate the use of a 80/20 acetone/water (A/W) solvent system (Ace‐SABRE) to provide hyperpolarized [1– 13 C]pyruvate with up to 17% polarization, then implement a solvent processing protocol to achieve injectable solutions retaining 74% of the initial polarization, and lastly we demonstrate HP in vivo spectroscopy and imaging using the Ace‐SABRE platform to showcase metabolic tracking in a hepatocellular carcinoma (HCC) tumor as well as HP‐MRI, both in direct comparison to dissolution dynamic nuclear polarization (d‐DNP) experiments. The Ace‐SABRE technique promises faster adoption of SABRE hyperpolarization in biological experiments, overall lowering the barriers to entry for HP‐NMR and HP‐MRI.}, journal={Angewandte Chemie International Edition}, author={McBride, Stephen J. and Pike, Megan and Curran, Erica and Zavriyev, Alexander and Adebesin, Bukola and Tucker, Luke and Harzan, Jared M. and Senanayake, Ishani M. and Abdulmojeed, Mustapha and Theiss, Franziska and et al.}, year={2025}, month={Apr} }